The concept of a semantic network or semantic net is now fairly old in the literature of cognitive science and artificial intelligence. The term “semantic network” dates back to Ross Quillian's Ph.D. thesis (1968), in which he first introduced it as a way of talking about the organization of human semantic memory, or memory for word concepts. The idea of a semantic network—that is, of a network of associatively linked concepts—is, however, very much older.
To get some feel for semantic nets, think of a common, but evocative, word, say, “home”. Write it down on a sheet of paper. Now think of some words related to home, say, “owner”, or “door”. Write down these words in a ring around “home”, and join each of them with a line to “home”. Now give each line a label that describes the relationship between the two words—for example, the line linking “home” and “owner” might be labelled “lives in”. Continue outwards, writing down words relating to “owner”, words relating to “door”, and so on. What you are constructing is, roughly, a semantic net.
The words of your drawing can be considered “nodes” of your network and the lines connecting these nodes are terms links (or, sometimes, arcs). Links of a semantic net are sometimes directed, meaning that a relationship between nodes exists only in one direction. One way to think of these links is as features of the nodes from which they emanate (the subject) and the nodes at the other end of the link (the target) might be the value of that feature.
Semantic nets thus have been a staple of computer study for a long time, and there has been much work done on traversal and decision algorithms for them. More recently, it has been noticed what a natural fit they make to the notation scheme provided by the Extensible Mark-up Language (XML). Less developed, though, is the use of semantic nets in object model definition and persistence.